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Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP ...
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Acquiring verb classes through bottom-up semantic verb clustering ...
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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction ...
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Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation ...
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Investigating the cross-lingual translatability of VerbNet-style classification. ...
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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction
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Investigating the cross-lingual translatability of VerbNet-style classification.
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Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation
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Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine.
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Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine.
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Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP
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Vulic, Ivan; Ponti, Edoardo; Reichart, Roi. - : Association for Computational Linguistics, 2018. : Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), 2018
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Acquiring verb classes through bottom-up semantic verb clustering
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Abstract:
In this paper, we present the first analysis of bottom-up manual semantic clustering of verbs in three languages, English, Polish and Croatian. Verb classes including syntactic and semantic information have been shown to support many NLP tasks by allowing abstraction from individual words and thereby alleviating data sparseness. The availability of such classifications is however still non-existent or limited in most languages. While a range of automatic verb classification approaches have been proposed, high-quality resources and gold standards are needed for evaluation and to improve the performance of NLP systems. We investigate whether semantic verb classes in three different languages can be reliably obtained from native speakers without linguistics training. The analysis of inter-annotator agreement shows an encouraging degree of overlap in the classifications produced for each language individually, as well as across all three languages. Comparative examination of the resultant classifications provides interesting insights into cross-linguistic semantic commonalities and patterns of ambiguity.
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URL: https://www.repository.cam.ac.uk/handle/1810/279155 https://doi.org/10.17863/CAM.26535
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